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van Gelder and Van Daele Crime Science 2014, 3:6
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EDITORIAL
Open Access
Innovative data collection methods in
criminological research: editorial introduction
Jean-Louis van Gelder1* and Stijn Van Daele2
Abstract
Novel technologies, such as GPS, the Internet and virtual environments are not only rapidly becoming an
increasingly influential part of our daily lives, they also have tremendous potential for improving our understanding
of where, when and why crime occurs. In addition to these technologies, several innovative research methods, such
as neuropsychological measurements and time-space budgets, have emerged in recent years. While often highly
accessible and relevant for crime research, these technologies and methods are currently underutilized by criminologists
who still tend to rely on traditional data-collection methods, such as systematic observation and surveys.
The contributions in this special issue of Crime Science explore the potential of several innovative research
methods and novel technologies for crime research to acquaint criminologists with these methods so that they
can apply them in their own research. Each contribution deals with a specific technology or method, gives an
overview and reviews the relevant literature. In addition, each article provides useful suggestions about new ways in
which the technology or method can be applied in future research. The technologies describe software and hardware
that is widely available to the consumer (e.g. GPS technology) and that sometimes can even be used free of charge
(e.g., Google Street View). We hope this special issue, which has its origins in a recent initiative of the Netherlands
Institute for the Study of Crime and Law Enforcement (NSCR) called CRIME Lab, and a collaborative workshop together
with the Research consortium Crime, Criminology and Criminal Policy of Ghent University, will inspire researchers to
start using innovative methods and novel technologies in their own research.
Issues and challenges facing crime researchers
Applied methods of scientific research depend on a variety
of factors that play out at least in part beyond researcher
control. One such factor regards the nature and availability of data, which largely determines the research questions that can be addressed: Data that cannot be collected
cannot serve to answer a research question. Although this
may seem like stating the obvious, it has had and continues to have far-reaching implications for criminological
research. Because crime tends to be a covert activity and
offenders have every interest in keeping it that way, crime
in action can rarely be observed, let alone in such a way as
to allow for systematic empirical study. Consequently, our
knowledge of the actual offending process is still limited
and relies in large part on indirect evidence. The same applies to offender motivations and the offending process;
these cannot be measured directly in ways similar to how
* Correspondence: [email protected]
1
Netherlands Institute for the Study of Crime and Law Enforcement (NSCR),
De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
Full list of author information is available at the end of the article
we can observe say the development of a chemical process
or the workings of gravity. This has placed significant restrictions on the way crime research has been performed
over the years, and most likely has coloured our view of
crime and criminality by directing our gaze towards certain more visible elements such as offender backgrounds,
demographics and criminal careers.
Another factor operating outside of researcher control
regards the fact that the success of scientific analyses
hinges on the technical possibilities to perform them. Even
if researchers have access to particular data, this does not
guarantee that the technology to perform the analysis in
the best possible way is available to them. The complexity
of the social sciences and the interconnectivity of various
social phenomena, together with the lack of a controlled
environment or laboratory, require substantial computational power to test highly complicated models. For instance,
the use of space-time budgets, social network analysis or
agent-based modelling can be quite resource-intensive.
Adding the complexity of social reality to the resulting
datasets (e.g. background variables related to social
© 2014 van Gelder and van Daele; licensee Springer. This is an Open Access article distributed under the terms of the Creative
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van Gelder and Van Daele Crime Science 2014, 3:6
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structure, GIS to incorporate actual road networks, etc.)
puts high pressure on the available computational power
of regular contemporary desktop computers. Addressing these challenges has practical and financial consequences, which brings us to the third issue, which
although more mundane in nature is by no means limited
to the applied sciences: a researcher requires the financial
and non-financial resources to be able to perform his
research.
All three factors have a bearing on the contents and
goals of the present special issue. More specifically, the
methods that are discussed and explained in different articles in this special issue each address one or more of
these factors and have, we believe, the potential to radically change the way we think about crime and go about
doing crime research. The methods and technologies are
available, feasible and affordable. They are, so to speak,
there for the taking.
Notes on criminological research methods
The above factors have contributed to a general tendency
within criminology to stick to particular research traditions, both in terms of method and analysis. Although
these traditions are well-developed and have undergone
substantial improvement over the decades, most changes
in the way research is performed in the discipline have
been of the ’more of the same’ variety, and most involve
gradual improvements, rather than embodying ‘scientific
revolutions’ and ‘paradigm shifts’ that have radically changed how criminologists go about doing research and thinking about crime.
With respect to methodology, since the field’s genesis
in the early 20th century, research in criminology has
largely spawned studies using similar methods. In a metaanalysis of articles that have appeared in seven leading
criminology and criminal justice journals in 2001–2002,
Kleck et al. (2006) demonstrate that survey research is still
the dominant method of collecting information (45.1%),
followed by the use of archival data (31.8%), and official
statistics (25.6%). Other methods, such as interviews, ethnographies and systematic observation, each account for
less than 10% (as methods are sometimes combined the
percentages add up to more than 100%). Furthermore, the
Kleck et al. meta-analysis shows that as far as data analysis
is concerned, nearly three quarters (72.5%) of the articles
published uses some type of multivariate statistics. As
such, it appears that criminology has developed a research
tradition that started to dominate the field, though not to
everybody’s enthusiasm. Berk (2010), for example, states
that regression analysis, in its broad conception, has become so dominant that researchers give it more credit
than they sometimes should and take some of its possibilities for granted. Whereas certain research traditions may
have a strong added value, they run the risk of being used
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more out of habit than for being the most appropriate
method to answer a particular research question. As
innovation influences our daily lives and how we perform
our daily activities, there is no reason why it should not
influence our way of performing science as well.
Innovation and technology in daily life and their
application in criminological research
Novel technologies, such as GPS, the Internet, and virtual environments are rapidly establishing themselves
as ingrained elements of our daily lives. Think, for example, of smart phones. While unknown to most of us as
recently as ten years ago, today few people would leave
their home without carrying one with them. Through their
ability to, for example, transmit visual and written communication, use social media, locate ourselves and others
through apps and GPS, smart phone technology has radically changed the way we go about our daily routines. The
research potential of smart phone technology for improving our understanding of social phenomena is enormous
and includes a better understanding of where, when and
why crime occurs through automated data collection,
interactive tests and surveys (Miller 2012). Yet, so far surprisingly few crime researchers have picked up on the possibilities this technology has to offer.
A similar point can be made with respect to the Internet. While most researchers have made the transition
from paper-and-pencil questionnaires to online surveys
and online research panels may have become the norm
for collecting community sample data, much of the potential of the Internet for crime research remains untapped. Think, for example, of the possibility of using
Google Maps and Google Street View for studying
spatial aspects of crime and the ease with which we can
virtually travel down most of the roads and streets to
examine street corners, buildings and neighbourhoods.
The launch of Google Street View in Belgium stirred up
controversy because it was perceived by police officers
as a catalogue for burglars and was expected to lead to
an increase in the number of burglaries. While it is not
clear whether this has actually happened, crime studies
reporting these tools in their method sections are few
and far between.
In addition to novel technologies, several innovative
research methods, such as social network analysis, timespace budgets, and neuropsychological measures that
can tap into people’s physiological and mental state, have
emerged in recent years and have become common elements of the tool-set used in some of criminology’s sister
disciplines, such as sociology, psychology and neuroscience. Again, while often highly accessible and relevant for
crime research, these methods are currently underutilized
by crime researchers. The promising and emerging field of
van Gelder and Van Daele Crime Science 2014, 3:6
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neurocriminology is an important exception in this respect (see e.g., Glenn & Raine 2014).
To revert back to the three issues that have restricted the
gaze of criminology mentioned at the outset of this article:
accessibility of data, type of data analysis, and availability of
resources, the new methods and technologies seem to
check all the boxes. Much more data have become accessible for analysis through novel technologies, new methods
for data-analysis and data-collection have been developed,
and most of it is available at the consumer level. In other
words, this allows for collecting and examining data in ways
that go far beyond what we are used to in crime research.
For example, methods combining neurobiological measurements with virtual environments can tell us a lot about an
individual’s mental and physical states and how they go
about making criminal decisions, which were until recently
largely black boxes. Finally, novel technologies are often
available on the consumer market, such as smart phones,
tablets, and digital cameras, and require only very modest
research budgets. In some cases, such as the automated
analysis of footage made by security cameras, simulations,
or the use of honeypots that lure cyber criminals into hacking them, they even allow for systematically examining
crime in action.
Goals of this special issue of Crime Science
The principal aim of the present special issue is to acquaint
crime researchers and criminology students with some of
these technologies and methods and explain how they can
be used in crime research. While widely diverging, what
these technologies and methods have in common is their
accessibility and potential for furthering our understanding
of crime and criminal behavior. The idea, in other words,
is to explore the potential of innovative research methods
of data-collection and novel technologies for crime research and to acquaint readers with these methods so that
they can apply them in their own research.
Each article deals with a specific technology or method
and is set-up in such a way as to give readers an overview
of the specific technology/method at hand, i.e., what it is
about, why to use it and how to do so, and a discussion of
relevant literature on the topic. Furthermore, when relevant, articles contain technical information regarding the
tools that are currently available and how they can be obtained. Case studies also feature in each article, giving
readers a clearer picture of what is at stake. Finally, we
have encouraged all contributors to give their views on
possible future developments with the specific technology
or method they describe and to make a prediction regarding the direction their field could or should develop. We
sincerely hope this will inspire readers to apply these
methods in their own work.
While not intended as a manual, after reading an article, readers should not only have a clear view of what
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the method/technology entails but also of how to use it,
possibly in combination with one or more of the references that are provided in the annotated bibliography
provided at the end of each article. The contributions
may also be useful for researchers already working with
a specific method or students interested in research
methodology.
Article overview
Hoeben, Bernasco and Pauwels discuss the space-time
budget method. This method aims at retrospectively recording on an hour-by-hour basis the whereabouts and activities of respondents, including crime and victimization.
The method offers a number of advantages over existing
methods for data-collection on crime and victimization
such as the possibility of studying situational causes of
crime or victimization, possibly in combination with individual lifestyles, routine activities and similar theoretical
concepts. Furthermore, the space-time budget method offers
the possibility of collecting information on spatial location,
which enables the study of a respondent’s whereabouts, going beyond the traditional focus of ecological criminological
studies on residential neighborhoods. Finally, the collected
spatial information on the location of individuals can be
combined with data on neighborhood characteristics from
other sources, which enables the empirical testing of a variety of ecological criminological theories.
The contribution of Gerritsen focuses on the possibilities
offered by agent-based modeling, a technique borrowed
from the domain of Artificial Intelligence. Agent-based
modeling (ABM) is a computational method that enables a
researcher to create, analyse and experiment with models
composed of agents that interact within a computational
environment. Gerritsen explains how modeling and simulation techniques may help gain more insight into (informal) criminological theories without having to experiment
with these phenomena in the real world. For example, to
study the effect of bystander presence on norm violation,
an ‘artificial neighborhood’ can be developed that is inhabited by 'virtual aggressors and victims’, and by manipulating
the agents’ parameters different hypothetical scenarios
may be explored. This enables criminologists to investigate
complex processes in a relatively fast and cheap manner.
Since the pioneering early studies of the 1990s hinted
at its promise as a method for social scientific research,
Virtual Reality (VR) technology is increasingly used as a
research tool by social scientists from various disciplines
such as psychology and sociology. Given recent developments that have greatly enhanced the realism, reduced
costs, and increased possibilities for application, it seems
well on its way to become an established method in the
social sciences. In their contribution Van Gelder, Luciano
and Otte explore the potential of VR as a tool for research in criminology. Their article offers a description
van Gelder and Van Daele Crime Science 2014, 3:6
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of how virtual reality has been used in social scientific research before discussing the potential it holds for criminology. By way of illustration, the authors describe a
recent case study that has used immersive virtual reality
for theory testing, which gives an idea of the unique possibilities of VR for studying crime.
Cornet also draws from criminology’s sister disciplines
in her article on neurobiological measurements observing
that while the literature on the relationship between neurobiological factors and antisocial behavior has accumulated
in recent years, the use of neurobiological measurements
does not yet play a central role in criminological research.
One of the underlying reasons Cornet identifies is the idea
among criminologists that it is complicated to use these
techniques compared to sociological, psychological and
behavioral instruments. This article aims to provide an
overview of the most commonly used neurobiological
measurements, their added value and, importantly, how
they can be relatively easily implemented in criminological
research.
Vandeviver explains the possibilities and merits of online
mapping applications such as Google Maps and Google
Street View. While these technologies have evoked concern from law enforcement agencies that offenders could
exploit them, Vandeviver notes that for criminologists they
also open up several new perspectives to conduct research.
Elaborating on results from prior studies in related fields
and personal research experience, three potential uses of
Google Maps and Google Street View in criminological research are explored and discussed. In particular, this article
deals with how these online mapping applications can generate new research questions, how they allow revisiting
and re-assessing established theories in criminology and
common research practices, and how they should be considered an essential part of the methodological toolkit of
criminologists.
Lemieux, also taking a spatial approach, describes how
digital cameras with integrated GPS units can be used to
collect useful data for criminological purposes observing
that while visual observations have traditionally been an
important method of criminological research, when combined with spatial data, such observations become a particularly useful way to describe how crime and disorder
are distributed. Lemieux describes the utility and ease of
using geo-tagged photographs and GPS tracks for monitoring illegal activity in urban and rural settings presenting
case studies from Uganda and Amsterdam.
Vander Laenen discusses a method of qualitative criminological research, the Nominal Group Technique (NGT),
which is not necessarily innovative by and of itself, but
which has hardly been applied in criminology. The NGT
is a highly structured technique with characteristics of
an individual survey and a focus group. However its structure limits the influence of the researcher and of group
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dynamics and guarantees equal participation to all group
members and equal influence of (conflicting) values and
ideas. The NGT forms an ideal technique for gathering
and prioritizing ideas and for innovative solution planning.
Furthermore, despite the structure of the procedure, the
technique can be modified and easily be applied in a
mixed-methods research design. By means of an example,
Vande Laenen reports on the use of the NGT in a study
on the needs assessment of drug (prevention) policy.
In the final contribution to this special issue on innovative methods of data collection in criminology, De Bondt
suggests solutions for doing cross-border criminal policy
research and the confusion in wording and interpretation
that emerge when comparing different legal systems. Correctly interpreting foreign information and using it as a
valid source to substantiate analyses on where, when and
why crime occurs and/or how to improve the fight against
crime, often requires the expertise of people that are very
familiar with a certain legal system. Based on a literature
review and a number of successful experiences with the
method in past research projects, the strengths and weaknesses of working with (predominantly) multiple choice
based questionnaires sent to a single point of contact in
each of the EU member states is elaborated upon.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Both Jean-Louis van Gelder and Stijn van Daele contributed to all aspects of
the paper. Both authors read and approved the final manuscript.
Author details
1
Netherlands Institute for the Study of Crime and Law Enforcement (NSCR),
De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands. 2Ghent University,
Universiteitstraat 4, 9000, Ghent, Belgium.
Received: 17 April 2014 Accepted: 30 April 2014
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Cite this article as: van Gelder and Van Daele: Innovative data collection
methods in criminological research: editorial introduction. Crime Science
2014 3:6.